{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cd85d3f-248c-4e9b-a80f-6c46bc1da07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "from sklearn import datasets\n",
    "# 导入sklearn自带的iris数据集\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "179bbdd4-0bf6-4811-bf4c-92520ab95205",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn import svm\n",
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ecb1506-5dd9-427c-91ce-05fc8fc16518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f7717bc-b649-4f17-a45c-7431db54fc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16bf9d53-fd8d-487c-8fd5-37bae6fa024a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面将数据分成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d43f1b63-0f7e-4f87-a9cb-f89ad5ed793c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8874908e-1fc2-4b1b-a299-ee4b4455924f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c7087c9-5ab6-4d24-b894-7aec6bb88927",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "627295f9-c287-46a6-92d0-48ea9144b84f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据：\n",
      " [[7.3 2.9 6.3 1.8]\n",
      " [5.9 3.  5.1 1.8]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [4.9 3.6 1.4 0.1]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [4.9 3.1 1.5 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.7 3.  5.  1.7]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.1 3.5 1.4 0.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [5.6 2.9 3.6 1.3]]\n",
      "均值： [5.73125    3.09821429 3.48482143 1.08303571]\n",
      "标准差： [0.80178721 0.46309945 1.76307411 0.74843747]\n",
      "标准化后的数据：\n",
      " StandardScaler()\n"
     ]
    }
   ],
   "source": [
    "# Fit only to the training data\n",
    "X_scaled = scaler.fit(X_train)\n",
    "print(\"原始数据：\\n\", X_train)\n",
    "print(\"均值：\", scaler.mean_)\n",
    "print(\"标准差：\", scaler.scale_)\n",
    "print(\"标准化后的数据：\\n\", X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a91149d-9324-4190-ae60-32148fd42756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.95656652, -0.42801668,  1.59674432,  0.95794815],\n",
       "       [ 0.21046731, -0.21208034,  0.91611496,  0.95794815],\n",
       "       [ 0.33518869, -1.93957106,  0.29220472, -0.11094543],\n",
       "       [ 0.58463143, -0.42801668,  0.46236206,  0.28988966],\n",
       "       [-0.41313954,  1.29947405, -1.12577311, -1.179839  ],\n",
       "       [ 0.95879554,  0.21979235,  0.91611496,  1.22517154],\n",
       "       [ 1.08351692, -0.42801668,  0.6325194 ,  0.28988966],\n",
       "       [ 1.45768103,  0.00385601,  1.0862723 ,  1.35878324],\n",
       "       [ 1.20823829,  0.00385601,  1.19971053,  1.75961833],\n",
       "       [-1.0367464 ,  1.08353771, -1.18249222, -1.3134507 ],\n",
       "       [ 2.70489475,  1.51541039,  1.65346343,  1.22517154],\n",
       "       [ 2.33073063, -0.21208034,  1.76690166,  1.35878324],\n",
       "       [-0.1636968 , -0.21208034,  0.34892383,  0.28988966],\n",
       "       [ 1.33295966,  0.21979235,  1.36986787,  1.62600663],\n",
       "       [ 0.83407417, -0.42801668,  0.46236206,  0.28988966],\n",
       "       [ 1.20823829,  0.00385601,  0.68923851,  0.55711306],\n",
       "       [-0.78730366, -1.29176204, -0.27498641,  0.02266627],\n",
       "       [-0.78730366,  0.86760137, -1.18249222, -1.04622731],\n",
       "       [-0.66258229,  0.65166503, -1.18249222, -1.179839  ],\n",
       "       [-1.66035326, -0.42801668, -1.18249222, -1.179839  ],\n",
       "       [-0.28841817, -1.72363472,  0.29220472,  0.28988966],\n",
       "       [-0.1636968 , -0.64395302,  0.80267673,  1.22517154],\n",
       "       [ 0.33518869, -0.42801668,  0.57580028,  0.55711306],\n",
       "       [-1.16146777, -0.21208034, -1.18249222, -1.04622731],\n",
       "       [-0.28841817, -1.50769838,  0.17876649,  0.02266627],\n",
       "       [ 0.45991006, -0.64395302,  0.29220472,  0.28988966],\n",
       "       [-0.78730366,  0.65166503, -1.12577311, -1.179839  ],\n",
       "       [ 0.08574594, -0.85988936,  0.91611496,  1.09155985],\n",
       "       [-0.03897543,  1.51541039, -1.01233488, -1.04622731],\n",
       "       [-0.66258229, -0.85988936,  0.2354856 ,  0.42350136],\n",
       "       [-1.0367464 ,  0.00385601, -1.12577311, -1.179839  ],\n",
       "       [-1.16146777, -0.21208034, -1.18249222, -1.3134507 ],\n",
       "       [-0.91202503,  1.08353771, -1.18249222, -1.179839  ],\n",
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       "       [ 1.20823829, -0.21208034,  0.85939585,  0.82433645],\n",
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       "       [-1.16146777,  0.00385601, -1.069054  , -1.179839  ],\n",
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       "       [-0.91202503,  0.86760137, -1.069054  , -0.64539221],\n",
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       "       [-0.91202503,  0.65166503, -1.069054  , -0.91261561],\n",
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       "       [-0.03897543, -0.42801668,  0.40564294,  0.28988966],\n",
       "       [-0.28841817, -1.29176204,  0.29220472,  0.28988966],\n",
       "       [-1.0367464 , -1.50769838, -0.10482908, -0.11094543],\n",
       "       [ 0.08574594, -0.85988936,  0.34892383, -0.11094543],\n",
       "       [ 1.83184514,  0.21979235,  1.42658698,  0.95794815],\n",
       "       [-0.03897543,  2.81102844, -1.12577311, -0.91261561],\n",
       "       [-0.41313954,  0.65166503, -1.12577311, -0.91261561],\n",
       "       [ 0.08574594,  1.94728308, -1.29593045, -1.179839  ],\n",
       "       [-1.53563189, -1.72363472, -1.23921134, -1.04622731],\n",
       "       [ 0.83407417, -0.64395302,  1.19971053,  1.49239494],\n",
       "       [-0.78730366,  1.29947405, -1.12577311, -0.91261561],\n",
       "       [ 0.7093528 ,  0.65166503,  1.19971053,  1.75961833],\n",
       "       [-0.78730366,  0.43572869, -1.01233488, -0.77900391],\n",
       "       [-0.28841817,  0.86760137, -1.23921134, -1.179839  ],\n",
       "       [-1.78507463, -0.21208034, -1.35264956, -1.3134507 ],\n",
       "       [-0.78730366,  0.86760137, -1.18249222, -1.179839  ],\n",
       "       [ 0.45991006, -0.21208034,  0.6325194 ,  0.42350136],\n",
       "       [-1.41091052,  1.08353771, -1.40936868, -1.179839  ],\n",
       "       [-1.66035326, -0.21208034, -1.23921134, -1.179839  ],\n",
       "       [-0.41313954, -0.21208034,  0.57580028,  0.55711306],\n",
       "       [ 0.95879554, -0.21208034,  0.97283407,  1.22517154],\n",
       "       [-0.28841817,  2.37915576, -1.18249222, -1.179839  ],\n",
       "       [ 2.455452  ,  1.51541039,  1.82362077,  1.49239494],\n",
       "       [-1.28618914,  0.21979235, -1.23921134, -1.179839  ],\n",
       "       [ 2.08128789, -0.64395302,  1.48330609,  1.09155985],\n",
       "       [ 1.20823829,  0.00385601,  0.51908117,  0.42350136],\n",
       "       [ 0.7093528 , -0.64395302,  0.91611496,  0.55711306],\n",
       "       [-0.78730366,  1.51541039, -1.12577311, -1.04622731],\n",
       "       [-0.1636968 , -0.85988936,  0.40564294,  0.28988966],\n",
       "       [ 1.33295966, -0.21208034,  1.14299141,  1.35878324],\n",
       "       [ 0.58463143, -1.93957106,  0.57580028,  0.55711306],\n",
       "       [ 0.45991006, -0.21208034,  0.80267673,  0.95794815],\n",
       "       [ 0.33518869,  0.65166503,  0.57580028,  0.69072475],\n",
       "       [-0.91202503,  0.21979235, -1.29593045, -1.179839  ],\n",
       "       [ 0.33518869, -0.85988936,  0.91611496,  0.69072475],\n",
       "       [ 0.33518869, -0.21208034,  0.74595762,  0.95794815],\n",
       "       [ 1.20823829,  0.43572869,  1.25642964,  1.89323003],\n",
       "       [ 0.33518869, -1.93957106,  0.85939585,  0.55711306],\n",
       "       [ 0.58463143,  0.65166503,  1.0862723 ,  1.62600663],\n",
       "       [ 0.83407417, -0.85988936,  1.02955319,  1.09155985],\n",
       "       [-1.41091052,  0.21979235, -1.18249222, -1.179839  ],\n",
       "       [-0.78730366,  1.51541039, -1.069054  , -1.179839  ],\n",
       "       [-0.41313954,  0.65166503, -1.01233488, -1.179839  ],\n",
       "       [ 1.45768103,  0.00385601,  0.91611496,  1.62600663],\n",
       "       [-0.91202503, -2.37144375,  0.00860915, -0.11094543],\n",
       "       [-0.53786091,  1.29947405, -1.12577311, -1.179839  ],\n",
       "       [ 0.7093528 , -0.42801668,  1.19971053,  0.95794815],\n",
       "       [-0.03897543, -1.0758257 ,  0.00860915, -0.11094543],\n",
       "       [-1.66035326,  0.21979235, -1.23921134, -1.179839  ],\n",
       "       [ 0.83407417,  0.00385601,  1.14299141,  0.95794815],\n",
       "       [-0.03897543, -0.64395302,  0.34892383,  0.28988966],\n",
       "       [-1.16146777,  0.65166503, -0.89889666, -1.179839  ],\n",
       "       [-0.1636968 , -1.29176204,  0.2354856 ,  0.02266627],\n",
       "       [-0.03897543, -0.21208034,  0.40564294,  0.15627797],\n",
       "       [ 0.08574594, -0.85988936,  0.2354856 ,  0.15627797],\n",
       "       [-0.66258229,  2.16321942, -1.12577311, -1.3134507 ],\n",
       "       [-1.41091052,  0.65166503, -1.18249222, -1.04622731],\n",
       "       [-0.41313954,  1.73134674, -1.01233488, -0.91261561],\n",
       "       [-0.78730366,  1.51541039, -0.89889666, -0.91261561],\n",
       "       [-1.16146777,  0.65166503, -1.069054  , -1.179839  ],\n",
       "       [ 0.45991006, -0.64395302,  0.68923851,  0.15627797],\n",
       "       [ 0.08574594, -0.85988936,  0.91611496,  1.09155985],\n",
       "       [-0.1636968 , -0.42801668,  0.06532826,  0.28988966]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now apply the transformations to the data:\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4360cb6-091c-4c9a-8956-b3261b71cc6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "001b269a-2eee-4127-8dc9-e53684c52c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(4,4,4),max_iter=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fc1a990-ee78-4f15-9878-c43279503fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(4, 4, 4), max_iter=1024)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=(4, 4, 4), max_iter=1024)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(4, 4, 4), max_iter=1024)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4874c84-bdf4-4123-9af5-4e13f285a7f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff371fb8-0398-4028-8887-2ac6c9e313be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1fe58a7f-17e0-40d1-96a5-cd5b8ee1032b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  0  0]\n",
      " [ 0  0 15]\n",
      " [ 0  0 17]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(y_test,predictions), display_labels=['0','1','2'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a6b179b-f3b3-440f-a9af-9bdf1cb004c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         6\n",
      "           1       0.00      0.00      0.00        15\n",
      "           2       0.53      1.00      0.69        17\n",
      "\n",
      "    accuracy                           0.61        38\n",
      "   macro avg       0.51      0.67      0.56        38\n",
      "weighted avg       0.40      0.61      0.47        38\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "# 利用模型对测试集进行预测，输出target预测标签值和概率\n",
    "y_test_pred = mlp.predict(X_test)\n",
    "y_test_prob = mlp.predict_proba(X_test)\n",
    "\n",
    "# 分类评估汇总报告classification_report\n",
    "print(classification_report(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb7d47fc-ece9-4b25-8fb6-024cfb544d4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5bc0c6-75e7-44e3-b37f-0cf5b9d4ee79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4b84d452-6682-416b-8339-9a0c537f101c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01816f65-4fda-45cc-9419-102eacdba425",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>14.23</td>\n",
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       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
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       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
       "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
       "1   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "\n",
       "     13  \n",
       "0  1065  \n",
       "1  1050  \n",
       "2  1185  \n",
       "3  1480  \n",
       "4   735  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('wine.txt',header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b26b73e8-7d82-4fb7-baff-e681e5f32a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,1:14]\n",
    "y=df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "acfe096e-cdae-4413-a822-79474604096d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73e9dea7-0ed4-4650-9246-9bc0852f2809",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pca降维\n",
    "from sklearn.decomposition import PCA \n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "197da644-da61-4c6d-82b0-e73c3dd46092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "391cf1dd-2464-40cc-94f0-cb240811cfd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ori=X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "005eda33-495f-4ac8-99b9-ef5d30ede53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.51861254, -0.5622498 ,  0.23205254, -1.16959318,  1.91390522,\n",
       "         0.80899739,  1.03481896, -0.65956311,  1.22488398,  0.25171685,\n",
       "         0.36217728,  1.84791957,  1.01300893],\n",
       "       [ 0.24628963, -0.49941338, -0.82799632, -2.49084714,  0.01814502,\n",
       "         0.56864766,  0.73362894, -0.82071924, -0.54472099, -0.29332133,\n",
       "         0.40605066,  1.1134493 ,  0.96524152],\n",
       "       [ 0.19687903,  0.02123125,  1.10933436, -0.2687382 ,  0.08835836,\n",
       "         0.80899739,  1.21553297, -0.49840699,  2.13596773,  0.26901965,\n",
       "         0.31830389,  0.78858745,  1.39514818],\n",
       "       [ 1.69154964, -0.34681064,  0.4879264 , -0.80925118,  0.93091845,\n",
       "         2.49144552,  1.46652465, -0.98187536,  1.03215473,  1.18606801,\n",
       "        -0.42754369,  1.18407144,  2.33457383],\n",
       "       [ 0.29570023,  0.22769377,  1.84040254,  0.45194578,  1.28198515,\n",
       "         0.80899739,  0.66335127,  0.22679555,  0.40140444, -0.31927553,\n",
       "         0.36217728,  0.44960118, -0.03787401],\n",
       "       [ 1.48155459, -0.51736664,  0.30515936, -1.28970717,  0.86070511,\n",
       "         1.56209322,  1.36612798, -0.17609475,  0.66421706,  0.73186953,\n",
       "         0.40605066,  0.33660575,  2.23903902],\n",
       "       [ 1.71625494, -0.4186237 ,  0.30515936, -1.46987817, -0.26270834,\n",
       "         0.32829793,  0.49267693, -0.49840699,  0.6817379 ,  0.08301456,\n",
       "         0.2744305 ,  1.36768901,  1.72952002],\n",
       "       [ 1.3086175 , -0.16727801,  0.89001391, -0.56902319,  1.49262517,\n",
       "         0.48853108,  0.48263726, -0.41782893, -0.59728351, -0.00349944,\n",
       "         0.44992405,  1.36768901,  1.74544249],\n",
       "       [ 2.25977152, -0.62508622, -0.7183361 , -1.65004916, -0.192495  ,\n",
       "         0.80899739,  0.95450162, -0.57898505,  0.6817379 ,  0.06138606,\n",
       "         0.53767082,  0.33660575,  0.94931905],\n",
       "       [ 1.0615645 , -0.88540853, -0.352802  , -1.04947918, -0.12228166,\n",
       "         1.09741707,  1.12517596, -1.14303148,  0.45396697,  0.93517742,\n",
       "         0.23055711,  1.32531572,  0.94931905]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std = StandardScaler()\n",
    "X = std.fit_transform(X_ori*1.0)\n",
    "X_all_ori=x.values\n",
    "X_all = std.fit_transform(X_all_ori*1.0)\n",
    "X_all[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06d24141-02cd-445f-aba1-b778df2ca548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy.cluster import hierarchy\n",
    "dendro=hierarchy.dendrogram(hierarchy.linkage(X,method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2c0cffa3-9d6d-4098-9ac9-63a7d39acc7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss: sum of dist. of sample to their closest cluster center')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,10),wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of cluster')\n",
    "plt.ylabel('wcss: sum of dist. of sample to their closest cluster center' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7c13fec3-5f7f-464d-a604-e4328e5ab0a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86147\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans_1=KMeans(n_clusters=3)\n",
    "kmeans_1.fit(X)\n",
    "cluster_pred=kmeans_1.predict(X)\n",
    "cluster_pred_2=kmeans_1.labels_\n",
    "cluster_center=kmeans_1.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ca383ce-75f4-453d-8353-cf765b7dacbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1,\n",
       "       2, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 1,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 2, 1, 1, 0, 2, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 2, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "607a6d93-4d89-4a89-b464-d8c2d93dd38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def show_confusion_matrix(cnf_matrix, class_labels):\n",
    "    plt.matshow(cnf_matrix, cmap=plt.cm.YlGn, alpha=0.7)\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlabel('Predicted Label', fontsize=16)\n",
    "    ax.set_xticks(range(0,len(class_labels)))\n",
    "    ax.set_xticklabels(class_labels,rotation=45)\n",
    "    ax.set_ylabel('Actual Label', fontsize=16, rotation=90)\n",
    "    ax.set_yticks(range(0,len(class_labels)))\n",
    "    ax.set_yticklabels(class_labels)\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    ax.xaxis.tick_top()\n",
    "\n",
    "    for row in range(len(cnf_matrix)):\n",
    "        for col in range(len(cnf_matrix[row])):\n",
    "            ax.text(col, row, cnf_matrix[row][col], va='center', ha='center', fontsize=32)\n",
    "\n",
    "class_labels = [0,1,2,3]\n",
    "\n",
    "cnf_matrix = confusion_matrix(y, cluster_pred) \n",
    "show_confusion_matrix(cnf_matrix, class_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0aca5ef7-63cf-4cc8-89e2-a22e2ac94756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.768361581920904\n"
     ]
    }
   ],
   "source": [
    "a=3+11+10+14+3\n",
    "b=177\n",
    "print(1-a/b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9b6d269-fa06-4e4f-aae9-9565be1c09d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(X[cluster_pred==0,0],X[cluster_pred==0,1], s = 100, c = 'red', label ='cluster 1' )\n",
    "plt.scatter(X[cluster_pred==1,0],X[cluster_pred==1,1], s = 100, c = 'blue', label ='cluster 2' )\n",
    "plt.scatter(X[cluster_pred==2,0],X[cluster_pred==2,1], s = 100, c = 'green', label ='cluster 3' )\n",
    "\n",
    "plt.scatter(cluster_center[:,0],cluster_center[:,1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69a6c28e-b7f8-4607-87d7-62204367609a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
